Doctor of Philosophy (PhD)


Physics and Astronomy

Document Type



Implementation of automation routines leveraging deep learning (DL) methods has been a growing topic of interest. The focus of this work is on the Gamma Knife (GK) workflow, specifically the approximation of dose distributions from GK plan parameters and the prediction of dose distributions using DL. These works contribute towards the larger goal of treatment plan prediction and are seen as intermediate steps towards that end. The approximation of dose distributions was motivated by the closed nature of the GK system, which causes complications with the evaluation of the predictions made by the DL models. The approximation utilizes a superposition method, accumulating dose from a basis set of dose distributions which correspond to specific sector activations of the GK system. The approximation method was compared to the original dose distributions calculated by the GK treatment planning system (TPS), Leksell GammaPlan, for 30 patients using the similarity metrics of Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) as well as the plan quality metrics of coverage, selectivity, and gradient index. In the range of clinical planning significance, the metrics reported strong agreement to the original calculation (DSC > 0.85, HD < 2.5mm, coverage difference = 0.014 ± 0.035, selectivity difference = 0.008 ± 0.037, and gradient index difference = 0.118 ± 0.192). The second focus of this work was to improve the performance of DL models used to predict dose distributions from target contour information. This was accomplished by applying transforms to the binary target contour volumes, resulting in alternate model input channels that encode the distance of each voxel to the boundary of the nearest contour, both within and outside of the contours. Training and evaluation were performed with a standard UNet and HD-UNet architecture for large and small targets separately. DSC and Predictions resulting from these encodings resulted in significantly better predictions (p < 0.05) than their binary target counterparts at all (excluding 90% isodose for the HD-Unet) threshold levels for small targets. The large target trainings were significantly (p < 0.05) improved in the mid-to-high dose ranges.



Committee Chair

Solis, David

Available for download on Monday, April 20, 2026